XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning
This work addresses outlier detection in practical datasets, offering an incremental improvement by hybridizing existing supervised and unsupervised techniques.
The authors tackled outlier detection by proposing XGBOD, a semi-supervised ensemble algorithm that combines unsupervised representation learning with supervised classification, resulting in superior performance compared to competing methods across seven datasets.
A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.